The subject matter disclosed herein relates generally to x-ray imaging systems, and more particularly to x-ray imaging systems that generate three-dimensional (3D) images.
X-ray imaging systems have many different configurations and are used in many different applications. For example, known x-ray absorption based imaging systems cover a wide range of imaging geometries, such as helical or step-and-shoot type computed tomography (CT) scanners, mammography or radiology-tomography (rad-tomo) systems with single arc/line/two-dimensional (2D) source trajectories, multi-view security systems, laminography type inspection systems, among others. The helical/step-and-shoot type full CT scanners typically acquire about 1000 or more views circularly around an object (e.g., a patient or luggage) for imaging each slice of a 3D volume of the object. These scanners are suitable when accurate 3D representation of imaging volume is needed. On the other hand, mammography or rad-tomo type systems or multi-view baggage scanning systems typically involve a much smaller number of views (e.g., about 5-60 views) and are suitable when the application requires viewing the 3D image volume only along certain specified orientations.
Accordingly, there is a wide gap in the application space between full CT scanners and other X-ray systems, due to the 3D image quality that is conventionally achievable with these different types of systems. For example, in x-ray tomosynthesis systems, planar two-dimensional (2D) or arc type source trajectories can acquire 3D depth information. However, when conventional reconstruction methods are employed for image formation, the resultant images are highly degraded. In particular, filtered backprojection type approaches often result in severe streaking in the images, and iterative approaches (even those based on using image domain priors or constraints, which can mitigate artifact levels to some extent) suffer from very slow convergence properties due to the highly incomplete nature of the data, thus resulting in images that lack high frequency details.